Comparing the efficacy of data-driven noise regression techniques in preserving age-related resting-state connectivity information

Data-driven denoising methods (global-signal regression (GS), white matter and CSF (cerebrospinal fluid) regression, anatomical and temporal CompCor, ICA AROMA) have been shown to remove cardiac and respiratory contributions from the fMRI signal. In this study, we compared the effectiveness of these methods in preserving the signals associated with age-related brain connectivity changes. We show that GS and AROMA resulted in diminished age-related brain connectivity differences, aCompCor and tCompCor retained the most connectivity differences while denoising effectively.